The information that can be derived from big data is endless. So much, that it will inevitably become difficult to distinguish trends from a mere coincidence, but at the pace that technology is developing, we won’t have to see those days. Up until recently, employees were asked to sit down with endless files and charts so that they can find the underlying trends to help promote growth in the company.

However, not only is this method inefficient, but it also leads to a burn out as well. Humans put to the task of analyzing data and recording trends from it will become tired very quickly and it limits their ability to find underlying trends. Thankfully, 2018 will see an end to this practice because machine learning is becoming widely implemented as a useful resource for big data management.

The Change to Data Science and Machine Learning

Not only does this mean a change in the big data analytics process itself, but it also means that more companies will be better able to achieve accurate results.

While machine learning isn’t a novelty in the world of technological developments, it certainly has gained more attention than ever. Big data analytics has been recognized as a viable method to learn about underlying trends in businesses and corporations.

Now that data science has achieved the recognition it deserves. Machine learning is in the spotlight for being effective since it allows computing devices to analyze data and derive trends on a logical basis. The results will be more accurate, and this owed largely to the nature of computers i.e. they never get tired.

Artificial Intelligence (AI) and Machine Learning

As the popularity of big data analytics grows, so will that of machine learning because the two are simultaneously linked. Confusion in the concepts of artificial intelligence and machine learning has emerged because of the staunch similarity between the two.

The two concepts have become synonymous and are often interchangeably used by big data enthusiasts and advocates. There’s no difference really, except that artificial intelligence is an umbrella term and machine learning lie underneath it as a current application.

Application of Big Data in Industries

This year will bring in countless developments but will also show many companies favoring the use of big data. Companies have struggled for decades to learn about behavioral patterns and trends that are displayed by consumers and data science holds the key.

Healthcare and financial institutions are just an example of the sectors that have made the change towards the application of big data analytics and machine learning processes. The activity and behavior carried out in banking institutions are being monitored using it track any suspicious and illegal activity occurring. Medical research facilities are looking into the opportunities that analytics will provide in enabling them to determine the spread and rise of chronic diseases in an area.

Tech and service industries will follow suit, but it will take more than this year for the world to completely adapt to this new change. This is because it’s still difficult to invest in these types of systems.

The Future of Analytics: Machine Learning

As explained before, machine learning algorithms aren’t a novelty, social media mammoth Facebook and reigning search engine Google implement them, and it’s only the beginning. The last couple of years showed a booming increase in the use of such techniques because of their ability to produce a predictive pattern when combined with data analytics. Data science is the hot new buzzword and many companies are proving it to be true. They keep big data as their primary asset because it boasts their overall experience. It proves that each deal, transaction, trading partner and complaint offer something to learn.

Data Science Helps Businesses Save and Reap Profits

No longer are organizations stuck to analyzing previous failures. With the help of current artificial intelligence, they can predict future highs and lows. The reason why it’s described as an invaluable resource is that machine learning can use existing data to help guide a business to success by guiding them along the right lines. Aside from being accurate, these systems help in generating a massive gain and every penny saved equals to millions of dollars in profit.

The 2018 Job Market and Need for Data Engineers

The fact that these systems can outperform human ability in each way may sound like bad news to a lot of current employees who are entrusted with the task of deriving predictions from heaps of data. However, you’d be surprised to know how many of current job postings on LinkedIn are related to big data and machine learning. The job forecast for 2018 shows that there will be a steady demand for engineers specialized in machine learning and most of them have to do with the application of data science.

Such skills are highly demanded in various industries across the market including advertising, sales, and even management. India is just one of the countries attempting to join in on the race to apply machine learning and data analysis strategies. This is because of reports that show that the job market will face a huge increase in the need for professionals that possess skills related to artificial intelligence and its uses. The reason for the increase in need is since no matter how much these systems learn, they’ll still lack the ability to make emotion and sentiment-based decisions that humans can.

Machine Learning Needs Data Scientists

Data scientists are conducting research on the various uses of machine learning-based analytics and detection of credit card fraud is one on a laundry list of others. A learning algorithm can use information about a certain transaction and can find chances of fraud by comparing it to previous transactions that share similar aspects.

The incorporation of artificial intelligence by various tech giants brings about convenience to our everyday lives. Gmail can identify certain emails as being spammy in nature while free music radio Pandora can make a playlist almost instantaneously based on our listening history and preferences. We take these features for granted but it only proves how well-suited they are to match our needs because of their seamless integration into our lives. Perhaps the most interesting aspect of it all is that unlike the conventional method of processing data analytics that become slower and less accurate as the piles of data grow large, machine learning systems perform better with the increase of data.

The Future of Marketing: Big Data and Machine Learning

It won’t be long until you’ll see the marketing adapt to this new style of deciphering trends because even though B2C businesses have constantly relied upon data to provide them with an insight into trends, things have changed. The dynamics of the situation revolving around the fact are that there is just too much data, but when it’s all unstructured, what valuable insights will they get?

Marketing firms are adapting to the craze of using machine learning as a way of structuring big data and receiving valid results.

Customer Journey Optimization

Ever since the internet happened, marketers have found it increasingly difficult to map the customer journey because it’s become non-linear. Now, brands must take care of their image, online reputation, and negative reviews, as if producing quality products wasn’t enough? There isn’t a straight path that consumers walk on but rather they follow a pattern of analyzing and selecting products. But figuring out the problem doesn’t equal to solving it and marketing firms need help in meeting consumers along a similar pattern. This is where our stars, machine learning, and data science play the role of being able to determine the desires of consumers.

Machine Learning Will Help in Automated Customer Service

Automation is nothing new and you see it every day when people use their smart devices to book flights, order food etc. Now, machine learning will increase the capabilities of automated software by allowing it to predict patterns based on a customer’s queries and complaints. This makes them well-suited to recommend further services. A perfect example of this is the AI bot of MasterCard called MasterCard KAI which recognizes a pattern of spending by a consumer and then recommends financial services. When it is applied to businesses, improved learning algorithms can give them an advantage because access to structured data can build a seasoned salesman who can effectively sell products.

Most of all, predictive analytics will help them make better decisions with regards to advertising. The resulting ads will be worth the cost because they’re developed through a predictive process based on real statistics, not just what marketers think people want.

Artificial Intelligence Changing the Face of the Financial Sector

By reducing the risks of potential error, machine learning systems can help build an established financial setup. Data science will provide financial institutions with predictive patterns that can help them minimize the chances of falling victim to financial risk. Complicated reports can be derived and written without the need for human effort, but the best part is that they will be more accurate and in-depth so that companies are guided every step of the way.

Implementation of Data Engineering and Machine Learning by B2B Companies

While most of the focus regarding the implementation of data science shifts towards B2C companies, it’s important to know that B2B corporations also require the aspects of the new technology. B2B companies should join the race, lest they risk losing potential customers whom they have yet to deal with. The reason for this is that customers, no matter if it’s an individual or business, always have similar expectations regarding the quality, service, and price of what they’re purchasing.

Considering that the cycle of a B2B purchase is longer and more complex, experts suggest that it’s highly significant that those companies set their sights on acquiring big data structuring and analysis with the help of machine learning systems. There are currently many ways that the use of machine learning and data engineering can benefit B2B companies.

Lead Generation with Data Science

For B2B companies, generating leads is no easy process and can take more than hundreds of man-hours to complete. They’ll have to collect and compile data through research and visiting company websites, LinkedIn accounts and more. Artificial intelligence will prove to be immensely effective in the process because the system doesn’t just collect data, but it analyzes it as well. By structuring big data such as records of emails, social media posts, and phone calls, a machine learning software can decipher who fits the bill for a prospective customer.

Machine Learning Helps in Management of Accounts

When analyzing crucial data, machine learning software can derive useful patterns that can help the cause of your marketing and sales team. Through thorough inspection of the set of records and other important data, the systems will separate a B2B company’s customers into brackets of good and bad based on how commonly they conduct transactions and other factors. Once that’s done, it’s up to the marketing team to apply special strategies to attract customers in both the brackets. Once the new findings are incorporated into the company’s efforts to find prospective clients, there’s no doubt that it’ll help in streamlining the process of scoring leads. This can help executives and managers prioritize what direction they should focus their marketing efforts on.

Big Data Changes to Expect in the Healthcare Sector

The healthcare industry often tops the list as the most rapidly growing industry to acquire the latest technology and that’s a given, considering that it’s a crucial sector of society. Because of this, it’s highly expected that the healthcare sector will begin operating using systems that involve the stratification of big data with the help of machine learning technology.

Diagnosis Through Data Analysis and Machine Learning

Predictions for this year call for the development of artificial intelligence to detect to work with big data to enable scientists and help them prevent chronic ailments. Artificial intelligence systems will be able to do so by thoroughly analyzing a patient’s medical records and history to understand how a disease works and begins to surface through symptoms. Rather than reacting, doctors will be better able to remain proactive.

Efficient and Smarter Prostheses with Artificial Intelligence

Moving forward, the use of artificial intelligence systems will be well-suited to the cause of developing intelligent and smarter prostheses that can prove to be life-changing for certain patients. These will be able to distinguish between concepts and feelings such as images, pressure, and light. This medical breakthrough will all be useful in giving disabled patients a better life.

Artificial Intelligence and Medical Assistants

The thought of robotic assistants isn’t far from surreal and straight up creepy but it’s very likely to become a reality. These assistants will be powered through an artificial intelligence system and can either be virtual or physical. While virtual systems can track heart rates and monitor other factors, they can constantly compare them to loads of big data to pick up an irregularity and quickly inform head surgeons or physicians. Robotics can be implemented here as well because they can physically monitor patients and notify a team of doctors in the case that any need arises.

The accuracy of machine learning technology surpasses that of a human, which is why the use of such systems is what will change the entire perspective of healthcare.

Kepler Space Telescopes Impact on Data

The massive Kepler Space Telescope was launched in 2009 and ever since then, it has collected a massive amount of data comprising around 14 billion data points and over 30 thousand signals. This vast amount of data could never be managed by humans alone because the amount of it is overwhelming and can’t be looked upon at once.

Machine data was just what the program needed because it was able to effectively decipher and examine enormous amounts of data through the application of intelligent algorithms. With a high accuracy of around 96 percent, the machine learning system differentiated between false signals and was able to identify actual planets.

The Role of Machine Learning in Big Data in 2018

In the latter part of 2018, it’s possible that a larger portion of technology companies will implement the use of artificial intelligence to spot imperfections with increased accuracy that exceeds that of a human’s capability. That’s not all, the heightened accuracy of machine learning systems will continue to improve since it’s not just a one-time process. As more data is analyzed by the learning algorithms, they’re bound to get stronger and more precise.

These applications make living with robots seem like a foreseeable part of mankind’s future. The current incorporation of artificial intelligence systems and data analysis make it seem like machines know our preferences better than we do and it makes our lives more convenient. Continuing development in the field of artificial intelligence and data science is sure to bring a much-needed change to the world by making it efficient and better able to help others.